Intelligent data-driven prognostic methodologies for the real-time remaining useful life until the end-of-discharge estimation of the Lithium-Polymer batteries of unmanned aerial vehicles with uncertainty quantification
Intelligent data-driven prognostic methodologies for the real-time remaining useful life until the end-of-discharge estimation of the Lithium-Polymer batteries of unmanned aerial vehicles with uncertainty quantification
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DOI：
10.1016/j.apenergy.2019.113677
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发表时间：
2019-11-15
期刊：
影响因子：
11.2
通讯作者：
Zarouchas, Dimitrios
中科院分区：
文献类型：
--
作者：
Eleftheroglou, Nick;Mansouri, Sina Sharif;Zarouchas, Dimitrios
本文利用放电电压作为无人机锂聚合物电池剩余使用寿命概率估算的关键指标。在实际飞行中获得的几个放电电压历史构成了内部开发的训练数据集。基于最新的数学模型和创新的数学模型，提出了三种数据驱动的预测方法，即梯度增强树、贝叶斯神经网络和非齐次隐式半马尔可夫模型。详细描述了各模型的训练和测试过程。未见数据中的剩余有用寿命预测从所有三种方法中获得。除了均值估计外，还量化了与点预测相关的不确定性，并提供了上下限置信度。在六次随机飞行中，从充满电的电池开始，对剩余的有用寿命预测进行了介绍和讨论，并强调了每种方法的利弊。几个特殊的指标被用来评估预测算法的性能，并得出关于它们的预测能力和潜力的结论。
In this paper, the discharge voltage is utilized as a critical indicator towards the probabilistic estimation of the Remaining Useful Life until the End-of-Discharge of the Lithium-Polymer batteries of unmanned aerial vehicles. Several discharge voltage histories obtained during actual flights constitute the in-house developed training dataset. Three data-driven prognostic methodologies are presented based on state-of-the-art as well as innovative mathematical models i.e. Gradient Boosted Trees, Bayesian Neural Networks and Non-Homogeneous Hidden Semi Markov Models. The training and testing process of all models is described in detail. Remaining Useful Life prognostics in unseen data are obtained from all three methodologies. Beyond the mean estimates, the uncertainty associated with the point predictions is quantified and upper/lower confidence bounds are also provided. The Remaining Useful Life prognostics during six random flights starting from fully charged batteries are presented, discussed and the pros and cons of each methodology are highlighted. Several special metrics are utilized to assess the performance of the prognostic algorithms and conclusions are drawn regarding their prognostic capabilities and potential.